Evaluation of multivariate distance drift diffusion framework for analysis of longitudinal omics and health data

Music City Center 

The transition from health to chronic disease inevitably involves changes in individual clinical biomarkers. These changes may be small when considered on a value-by-value basis over a short period of time; however, the aggregate signal of multiple measures may provide a practical way of detecting relevant change early and serve as an indicator for preventive interventions. Existing evidence suggest the importance of dynamics of evolution of omics and clinical measurements in biomedical data. This motivates the need for robust methodologies for modeling and inference with such longitudinal data. We have recently introduced a novel multivariate approach to longitudinal microbiome data analysis, multivariate distance drift-diffusion framework (MD3F). This framework allows to summarize multivariate trends over time in omics and health data. In this talk, we give examples of application of MD3F to microbiome and clinical laboratory data. We further discuss approaches for testing population differences in multivariate drift.